Functional genetic variations in splicing regulation

描述（由申请人提供）：最近，在构建疾病基因组或跨人群的遗传变异目录方面取得了巨大成功。下一个巨大的挑战是阐明各种遗传变异在生物和疾病过程中的潜在功能。一种重要类型的功能变体由影响基因顺式表达的那些组成。事实上，顺式调节变体参与了广泛的疾病，它们对基因表达的影响始终比反式作用决定簇更强。选择性剪接是顺式调节变化发生的重要机制。先前的研究估计，导致人类遗传疾病的15- 60%的点突变会破坏剪接，这突出了这一调控步骤的重要性。此外 与已知的剪接位点信号相比，剪接受许多外显子或内含子顺式调控 元素，与反式作用蛋白质相关。这些顺式调节元件的破坏可引起异常剪接。然而，这一关键的监管方面在很大程度上仍未得到探索。我们建议结合联合收割机计算，基因组和分子的方法来研究剪接的变化，由于遗传变异。具体目标是：（1）通过高通量转录组谱测序（RNA-Seq）的生物信息学分析，在全球范围内识别受遗传变异的替代等位基因差异剪接调控的外显子和基因。(2)使用基于小基因的实验鉴定剪接改变中的致病遗传变异。(3)利用机器学习方法、RNA-Seq数据和分子验证，开发一个预测剪接改变中因果遗传变异的综合模型。本项目将阐明剪接过程中的顺式调控基因变异，并对基因变异与人类疾病的关系提供重要的见解。此外，这项工作将产生有价值的生物信息学工具，以充分利用各种细胞类型中日益可用的RNA-Seq数据，用于识别和预测疾病相关的遗传变异。

DESCRIPTION (provided by applicant): Recently, tremendous success has been achieved in constructing a catalog of genetic variants in disease genomes or across population. The next great challenge is to elucidate the potential function of various genetic variants in biological an disease processes. An important type of functional variants consists of those that affect gene expression in cis. Indeed, cis-regulatory variants are involved in a broad range of diseases and they showed a consistently stronger influence on gene expression than trans-acting determinants. Alternative splicing is an essential mechanism via which cis-regulatory changes may occur. Previous studies estimated that 15- 60% of point mutations that result in human genetic diseases disrupt splicing, highlighting the importance of this regulatory step. In addition to the well-known splice site signals, splicing is closely regulated by many exonic or intronic cis elements, associated with trans-acting proteins. Disruption of these cis-regulatory elements can cause aberrant splicing. Yet this crucial regulatory aspect remains largely unexplored. We propose to combine computational, genomic and molecular approaches to study splicing changes due to genetic variations. The specific aims are: (1) To globally identify exons and genes that are under differential splicing regulation by the alternative alleles of genetic variant, via bioinformatic analysis of high-throughput sequencing of transcriptome profiles (RNA-Seq). (2) To identify causal genetic variants in splicing alteration using minigene-based experiments. (3) To develop an integrative model to predict causal genetic variants in splicing alteration, using machine learning approaches, RNA- Seq data and molecular validations. This project will elucidate functional cis-regulatory genetic variants in splicing and provide significant insight ino the involvement of genetic variations in human diseases. In addition, this work will generate valuable bioinformatic tools to make full use of the increasingly available RNA-Seq data in a wide variety of cell types for identification and prediction of disease-related genetic variants.